Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations104995
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.0 MiB
Average record size in memory120.0 B

Variable types

Numeric13
Categorical2

Alerts

C is highly overall correlated with DI and 6 other fieldsHigh correlation
DI is highly overall correlated with C and 4 other fieldsHigh correlation
DO is highly overall correlated with C and 4 other fieldsHigh correlation
L is highly overall correlated with C and 4 other fieldsHigh correlation
LD is highly overall correlated with L and 1 other fieldsHigh correlation
L_CYL is highly overall correlated with C and 4 other fieldsHigh correlation
MC is highly overall correlated with PR_NCCHigh correlation
PO is highly overall correlated with PR_NCCHigh correlation
PR_NCC is highly overall correlated with MC and 1 other fieldsHigh correlation
SA is highly overall correlated with C and 6 other fieldsHigh correlation
SA_CYL is highly overall correlated with C and 6 other fieldsHigh correlation
SA_D is highly overall correlated with C and 4 other fieldsHigh correlation
TL is uniformly distributed Uniform

Reproduction

Analysis started2024-11-08 04:49:24.284707
Analysis finished2024-11-08 04:50:29.445763
Duration1 minute and 5.16 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

C
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.004524
Minimum1
Maximum191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2024-11-08T04:50:29.632387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q151
median101
Q3151
95-th percentile184
Maximum191
Range190
Interquartile range (IQR)100

Descriptive statistics

Standard deviation57.660734
Coefficient of variation (CV)0.60060434
Kurtosis-1.2059878
Mean96.004524
Median Absolute Deviation (MAD)50
Skewness-2.2400002 × 10-5
Sum10079995
Variance3324.7602
MonotonicityNot monotonic
2024-11-08T04:50:30.036065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
101 5250
 
5.0%
11 5250
 
5.0%
181 5250
 
5.0%
171 5250
 
5.0%
161 5250
 
5.0%
151 5250
 
5.0%
141 5250
 
5.0%
131 5250
 
5.0%
121 5250
 
5.0%
111 5250
 
5.0%
Other values (10) 52495
50.0%
ValueCountFrequency (%)
1 5245
5.0%
11 5250
5.0%
21 5250
5.0%
31 5250
5.0%
41 5250
5.0%
51 5250
5.0%
61 5250
5.0%
71 5250
5.0%
81 5250
5.0%
91 5250
5.0%
ValueCountFrequency (%)
191 5250
5.0%
181 5250
5.0%
171 5250
5.0%
161 5250
5.0%
151 5250
5.0%
141 5250
5.0%
131 5250
5.0%
121 5250
5.0%
111 5250
5.0%
101 5250
5.0%

LD
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0000952
Minimum2
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2024-11-08T04:50:30.356058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.2
Q13
median4
Q35
95-th percentile5.8
Maximum6
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2110161
Coefficient of variation (CV)0.30274682
Kurtosis-1.2054283
Mean4.0000952
Median Absolute Deviation (MAD)1
Skewness-2.143191 × 10-5
Sum419990
Variance1.46656
MonotonicityNot monotonic
2024-11-08T04:50:30.668792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 5000
 
4.8%
4.2 5000
 
4.8%
5.8 5000
 
4.8%
5.6 5000
 
4.8%
5.4 5000
 
4.8%
5.2 5000
 
4.8%
5 5000
 
4.8%
4.8 5000
 
4.8%
4.6 5000
 
4.8%
4.4 5000
 
4.8%
Other values (11) 54995
52.4%
ValueCountFrequency (%)
2 4995
4.8%
2.2 5000
4.8%
2.4 5000
4.8%
2.6 5000
4.8%
2.8 5000
4.8%
3 5000
4.8%
3.2 5000
4.8%
3.4 5000
4.8%
3.6 5000
4.8%
3.8 5000
4.8%
ValueCountFrequency (%)
6 5000
4.8%
5.8 5000
4.8%
5.6 5000
4.8%
5.4 5000
4.8%
5.2 5000
4.8%
5 5000
4.8%
4.8 5000
4.8%
4.6 5000
4.8%
4.4 5000
4.8%
4.2 5000
4.8%

DI
Real number (ℝ)

High correlation 

Distinct418
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean309.25095
Minimum60.7892
Maximum526.3191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2024-11-08T04:50:31.010321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum60.7892
5-th percentile135.1941
Q1251.8473
median322.0753
Q3374.242
95-th percentile454.93954
Maximum526.3191
Range465.5299
Interquartile range (IQR)122.3947

Descriptive statistics

Standard deviation97.332247
Coefficient of variation (CV)0.31473548
Kurtosis0.085373933
Mean309.25095
Median Absolute Deviation (MAD)59.2269
Skewness-0.51427491
Sum32469804
Variance9473.5663
MonotonicityNot monotonic
2024-11-08T04:50:31.383373image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
356.9308 750
 
0.7%
273.4265 250
 
0.2%
347.6072 250
 
0.2%
339.3535 250
 
0.2%
344.8261 250
 
0.2%
350.6698 250
 
0.2%
363.6644 250
 
0.2%
370.9367 250
 
0.2%
378.828 250
 
0.2%
387.4367 250
 
0.2%
Other values (408) 101995
97.1%
ValueCountFrequency (%)
60.7892 250
0.2%
61.5215 250
0.2%
62.2904 250
0.2%
63.0993 250
0.2%
63.9519 250
0.2%
64.8525 250
0.2%
65.806 250
0.2%
66.8183 250
0.2%
67.8958 250
0.2%
69.0464 250
0.2%
ValueCountFrequency (%)
526.3191 250
0.2%
516.9687 250
0.2%
507.2671 250
0.2%
506.8221 250
0.2%
497.818 250
0.2%
497.1796 250
0.2%
489.9266 250
0.2%
488.4759 250
0.2%
486.6653 250
0.2%
481.2226 250
0.2%

L
Real number (ℝ)

High correlation 

Distinct420
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1191.9876
Minimum182.7825
Maximum2100.4989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2024-11-08T04:50:31.719335image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum182.7825
5-th percentile406.5047
Q1918.3571
median1195.837
Q31509.374
95-th percentile1860.4935
Maximum2100.4989
Range1917.7164
Interquartile range (IQR)591.0169

Descriptive statistics

Standard deviation420.73911
Coefficient of variation (CV)0.35297274
Kurtosis-0.41972197
Mean1191.9876
Median Absolute Deviation (MAD)290.5554
Skewness-0.1929808
Sum1.2515273 × 108
Variance177021.4
MonotonicityNot monotonic
2024-11-08T04:50:32.093744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
851.22 250
 
0.2%
1434.3406 250
 
0.2%
1604.2202 250
 
0.2%
1561.0261 250
 
0.2%
1517.2349 250
 
0.2%
1472.813 250
 
0.2%
1427.7232 250
 
0.2%
1381.9247 250
 
0.2%
1335.3721 250
 
0.2%
1288.015 250
 
0.2%
Other values (410) 102495
97.6%
ValueCountFrequency (%)
182.7825 245
0.2%
193.6127 250
0.2%
204.1727 250
0.2%
214.4844 250
0.2%
224.5672 250
0.2%
234.4387 250
0.2%
244.1145 250
0.2%
253.6085 250
0.2%
262.9331 250
0.2%
272.0992 250
0.2%
ValueCountFrequency (%)
2100.4989 250
0.2%
2063.182 250
0.2%
2054.9424 250
0.2%
2024.4639 250
0.2%
2018.4348 250
0.2%
2008.8797 250
0.2%
1984.2054 250
0.2%
1980.5565 250
0.2%
1973.1904 250
0.2%
1962.2879 250
0.2%

L_CYL
Real number (ℝ)

High correlation 

Distinct420
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean882.7366
Minimum91.3913
Maximum1750.4158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2024-11-08T04:50:32.452507image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum91.3913
5-th percentile277.6369
Q1589.0828
median867.4878
Q31170.2542
95-th percentile1529.179
Maximum1750.4158
Range1659.0245
Interquartile range (IQR)581.1714

Descriptive statistics

Standard deviation380.29494
Coefficient of variation (CV)0.43081361
Kurtosis-0.78694435
Mean882.7366
Median Absolute Deviation (MAD)288.7778
Skewness0.12471202
Sum92682929
Variance144624.24
MonotonicityNot monotonic
2024-11-08T04:50:32.820634image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
425.61 250
 
0.2%
1092.831 250
 
0.2%
1270.0077 250
 
0.2%
1221.6726 250
 
0.2%
1172.4088 250
 
0.2%
1122.1432 250
 
0.2%
1070.7924 250
 
0.2%
1018.2603 250
 
0.2%
964.4354 250
 
0.2%
909.1871 250
 
0.2%
Other values (410) 102495
97.6%
ValueCountFrequency (%)
91.3913 245
0.2%
105.6069 250
0.2%
119.1008 250
0.2%
131.9904 250
0.2%
144.3646 250
0.2%
156.2925 250
0.2%
167.8287 250
0.2%
179.0178 250
0.2%
189.8961 250
0.2%
200.4942 250
0.2%
ValueCountFrequency (%)
1750.4158 250
0.2%
1719.3183 250
0.2%
1700.642 250
0.2%
1687.0533 250
0.2%
1670.4288 250
0.2%
1653.5045 250
0.2%
1650.1512 250
0.2%
1639.0812 250
0.2%
1620.835 250
0.2%
1618.5362 250
0.2%

TL
Categorical

Uniform 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size820.4 KiB
3
21000 
5
21000 
7
21000 
9
21000 
1
20995 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters104995
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 21000
20.0%
5 21000
20.0%
7 21000
20.0%
9 21000
20.0%
1 20995
20.0%

Length

2024-11-08T04:50:33.145252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T04:50:33.423063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
3 21000
20.0%
5 21000
20.0%
7 21000
20.0%
9 21000
20.0%
1 20995
20.0%

Most occurring characters

ValueCountFrequency (%)
3 21000
20.0%
5 21000
20.0%
7 21000
20.0%
9 21000
20.0%
1 20995
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 104995
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 21000
20.0%
5 21000
20.0%
7 21000
20.0%
9 21000
20.0%
1 20995
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 104995
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 21000
20.0%
5 21000
20.0%
7 21000
20.0%
9 21000
20.0%
1 20995
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 104995
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 21000
20.0%
5 21000
20.0%
7 21000
20.0%
9 21000
20.0%
1 20995
20.0%

DO
Real number (ℝ)

High correlation 

Distinct2090
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319.25134
Minimum62.7892
Maximum544.3191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2024-11-08T04:50:33.708774image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum62.7892
5-th percentile137.1941
Q1261.7869
median331.4823
Q3384.6215
95-th percentile466.34081
Maximum544.3191
Range481.5299
Interquartile range (IQR)122.8346

Descriptive statistics

Standard deviation97.495636
Coefficient of variation (CV)0.30538834
Kurtosis0.084723015
Mean319.25134
Median Absolute Deviation (MAD)60.0219
Skewness-0.51164576
Sum33519794
Variance9505.3991
MonotonicityNot monotonic
2024-11-08T04:50:34.035619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
370.9308 150
 
0.1%
358.9308 150
 
0.1%
362.9308 150
 
0.1%
366.9308 150
 
0.1%
374.9308 150
 
0.1%
414.8855 50
 
< 0.1%
425.3291 50
 
< 0.1%
398.8855 50
 
< 0.1%
402.8855 50
 
< 0.1%
406.8855 50
 
< 0.1%
Other values (2080) 103995
99.0%
ValueCountFrequency (%)
62.7892 50
< 0.1%
63.5215 50
< 0.1%
64.2904 50
< 0.1%
65.0993 50
< 0.1%
65.9519 50
< 0.1%
66.7892 50
< 0.1%
66.8525 50
< 0.1%
67.5215 50
< 0.1%
67.806 50
< 0.1%
68.2904 50
< 0.1%
ValueCountFrequency (%)
544.3191 50
< 0.1%
540.3191 50
< 0.1%
536.3191 50
< 0.1%
534.9687 50
< 0.1%
532.3191 50
< 0.1%
530.9687 50
< 0.1%
528.3191 50
< 0.1%
526.9687 50
< 0.1%
525.2671 50
< 0.1%
524.8221 50
< 0.1%

SA_D
Real number (ℝ)

High correlation 

Distinct2090
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350057.38
Minimum12385.696
Maximum930801.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2024-11-08T04:50:34.360092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum12385.696
5-th percentile59131.729
Q1215300.81
median345199.92
Q3464747.53
95-th percentile683214.14
Maximum930801.45
Range918415.75
Interquartile range (IQR)249446.72

Descriptive statistics

Standard deviation184742.6
Coefficient of variation (CV)0.52774947
Kurtosis-0.18753062
Mean350057.38
Median Absolute Deviation (MAD)124506.26
Skewness0.3379054
Sum3.6754274 × 1010
Variance3.4129827 × 1010
MonotonicityNot monotonic
2024-11-08T04:50:34.688084image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
432250.6671 150
 
0.1%
404735.5328 150
 
0.1%
413806.7133 150
 
0.1%
422978.4247 150
 
0.1%
441623.4405 150
 
0.1%
540762.1886 50
 
< 0.1%
568329.2817 50
 
< 0.1%
499857.6 50
 
< 0.1%
509932.9507 50
 
< 0.1%
520108.8324 50
 
< 0.1%
Other values (2080) 103995
99.0%
ValueCountFrequency (%)
12385.6959 50
< 0.1%
12676.2829 50
< 0.1%
12985.0244 50
< 0.1%
13313.8125 50
< 0.1%
13664.8219 50
< 0.1%
14014.0273 50
< 0.1%
14040.5655 50
< 0.1%
14323.0188 50
< 0.1%
14443.9651 50
< 0.1%
14651.0851 50
< 0.1%
ValueCountFrequency (%)
930801.4495 50
< 0.1%
917171.4834 50
< 0.1%
903642.0483 50
< 0.1%
899097.0105 50
< 0.1%
890213.1441 50
< 0.1%
885702.0471 50
< 0.1%
876884.7709 50
< 0.1%
872407.6146 50
< 0.1%
866782.8962 50
< 0.1%
865314.7175 50
< 0.1%

SA_CYL
Real number (ℝ)

High correlation 

Distinct2100
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean921354.42
Minimum26813.952
Maximum2024123.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2024-11-08T04:50:35.011054image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum26813.952
5-th percentile131061.04
Q1561280.55
median912294.11
Q31278235
95-th percentile1709464.8
Maximum2024123.6
Range1997309.7
Interquartile range (IQR)716954.49

Descriptive statistics

Standard deviation472868.96
Coefficient of variation (CV)0.51323243
Kurtosis-0.79020115
Mean921354.42
Median Absolute Deviation (MAD)356121.05
Skewness0.054723019
Sum9.6737607 × 1010
Variance2.2360506 × 1011
MonotonicityNot monotonic
2024-11-08T04:50:35.383182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
571754.4231 50
 
< 0.1%
1182541.237 50
 
< 0.1%
1178424.926 50
 
< 0.1%
1166305.473 50
 
< 0.1%
1154186.021 50
 
< 0.1%
1142066.568 50
 
< 0.1%
1129947.116 50
 
< 0.1%
1133457.898 50
 
< 0.1%
1122032.716 50
 
< 0.1%
1110607.534 50
 
< 0.1%
Other values (2090) 104495
99.5%
ValueCountFrequency (%)
26813.9517 45
< 0.1%
27962.4083 50
< 0.1%
29110.8648 50
< 0.1%
29861.5598 50
< 0.1%
30259.3213 50
< 0.1%
31188.6554 50
< 0.1%
31407.7778 50
< 0.1%
32515.7509 50
< 0.1%
32579.3822 50
< 0.1%
33842.8465 50
< 0.1%
ValueCountFrequency (%)
2024123.631 50
< 0.1%
2002127.257 50
< 0.1%
1989098.498 50
< 0.1%
1980130.884 50
< 0.1%
1967727.6 50
< 0.1%
1958134.511 50
< 0.1%
1954569.578 50
< 0.1%
1952999.405 50
< 0.1%
1946356.703 50
< 0.1%
1936138.137 50
< 0.1%

SA
Real number (ℝ)

High correlation 

Distinct2100
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1271411.8
Minimum54214.698
Maximum2449763
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2024-11-08T04:50:35.715923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum54214.698
5-th percentile263411.72
Q1827744.43
median1333013.6
Q31752345.4
95-th percentile2147834.4
Maximum2449763
Range2395548.3
Interquartile range (IQR)924600.99

Descriptive statistics

Standard deviation593360
Coefficient of variation (CV)0.4666938
Kurtosis-0.83849761
Mean1271411.8
Median Absolute Deviation (MAD)456189.71
Skewness-0.28083735
Sum1.3349188 × 1011
Variance3.5207609 × 1011
MonotonicityNot monotonic
2024-11-08T04:50:36.059023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1146195.599 50
 
< 0.1%
1611845.402 50
 
< 0.1%
1653659.133 50
 
< 0.1%
1631814.901 50
 
< 0.1%
1610071.2 50
 
< 0.1%
1588428.03 50
 
< 0.1%
1566885.391 50
 
< 0.1%
1628172.115 50
 
< 0.1%
1606823.824 50
 
< 0.1%
1585576.064 50
 
< 0.1%
Other values (2090) 104495
99.5%
ValueCountFrequency (%)
54214.6981 45
< 0.1%
55311.7158 50
< 0.1%
56397.4588 50
< 0.1%
57464.8579 50
< 0.1%
57760.5986 50
< 0.1%
58510.306 50
< 0.1%
58951.1683 50
< 0.1%
59532.1608 50
< 0.1%
60132.7462 50
< 0.1%
60529.9172 50
< 0.1%
ValueCountFrequency (%)
2449762.969 50
< 0.1%
2424547.111 50
< 0.1%
2418565.922 50
< 0.1%
2398867.99 50
< 0.1%
2393869.549 50
< 0.1%
2387469.407 50
< 0.1%
2372704.639 50
< 0.1%
2368713.623 50
< 0.1%
2365946.402 50
< 0.1%
2363292.518 50
< 0.1%

PO
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451.02143
Minimum1
Maximum901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2024-11-08T04:50:36.346514image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1201
median501
Q3701
95-th percentile901
Maximum901
Range900
Interquartile range (IQR)500

Descriptive statistics

Standard deviation287.21955
Coefficient of variation (CV)0.63682019
Kurtosis-1.2241999
Mean451.02143
Median Absolute Deviation (MAD)200
Skewness-4.068472 × 10-5
Sum47354995
Variance82495.071
MonotonicityNot monotonic
2024-11-08T04:50:36.568318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
101 10500
10.0%
201 10500
10.0%
301 10500
10.0%
401 10500
10.0%
501 10500
10.0%
601 10500
10.0%
701 10500
10.0%
801 10500
10.0%
901 10500
10.0%
1 10495
10.0%
ValueCountFrequency (%)
1 10495
10.0%
101 10500
10.0%
201 10500
10.0%
301 10500
10.0%
401 10500
10.0%
501 10500
10.0%
601 10500
10.0%
701 10500
10.0%
801 10500
10.0%
901 10500
10.0%
ValueCountFrequency (%)
901 10500
10.0%
801 10500
10.0%
701 10500
10.0%
601 10500
10.0%
501 10500
10.0%
401 10500
10.0%
301 10500
10.0%
201 10500
10.0%
101 10500
10.0%
1 10495
10.0%

M_NAME
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size820.4 KiB
B5500
41998 
K-X09086
20999 
FLX40HP
20999 
HD614
20999 

Length

Max length8
Median length5
Mean length6
Min length5

Characters and Unicode

Total characters629970
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowK-X09086
2nd rowK-X09086
3rd rowK-X09086
4th rowK-X09086
5th rowK-X09086

Common Values

ValueCountFrequency (%)
B5500 41998
40.0%
K-X09086 20999
20.0%
FLX40HP 20999
20.0%
HD614 20999
20.0%

Length

2024-11-08T04:50:36.831036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T04:50:37.083703image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
b5500 41998
40.0%
k-x09086 20999
20.0%
flx40hp 20999
20.0%
hd614 20999
20.0%

Most occurring characters

ValueCountFrequency (%)
0 146993
23.3%
5 83996
13.3%
B 41998
 
6.7%
X 41998
 
6.7%
6 41998
 
6.7%
4 41998
 
6.7%
H 41998
 
6.7%
K 20999
 
3.3%
- 20999
 
3.3%
9 20999
 
3.3%
Other values (6) 125994
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 629970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 146993
23.3%
5 83996
13.3%
B 41998
 
6.7%
X 41998
 
6.7%
6 41998
 
6.7%
4 41998
 
6.7%
H 41998
 
6.7%
K 20999
 
3.3%
- 20999
 
3.3%
9 20999
 
3.3%
Other values (6) 125994
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 629970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 146993
23.3%
5 83996
13.3%
B 41998
 
6.7%
X 41998
 
6.7%
6 41998
 
6.7%
4 41998
 
6.7%
H 41998
 
6.7%
K 20999
 
3.3%
- 20999
 
3.3%
9 20999
 
3.3%
Other values (6) 125994
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 629970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 146993
23.3%
5 83996
13.3%
B 41998
 
6.7%
X 41998
 
6.7%
6 41998
 
6.7%
4 41998
 
6.7%
H 41998
 
6.7%
K 20999
 
3.3%
- 20999
 
3.3%
9 20999
 
3.3%
Other values (6) 125994
20.0%

PER_FIT
Real number (ℝ)

Distinct219
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4118554 × 10-15
Minimum1.16 × 10-17
Maximum7.26 × 10-14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2024-11-08T04:50:37.368481image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.16 × 10-17
5-th percentile2.11 × 10-17
Q11.11 × 10-16
median3.46 × 10-16
Q39.63 × 10-16
95-th percentile2.42 × 10-14
Maximum7.26 × 10-14
Range7.25884 × 10-14
Interquartile range (IQR)8.52 × 10-16

Descriptive statistics

Standard deviation1.0966733 × 10-14
Coefficient of variation (CV)3.2143019
Kurtosis0
Mean3.4118554 × 10-15
Median Absolute Deviation (MAD)2.827 × 10-16
Skewness0
Sum3.5822776 × 10-10
Variance1.2026924 × 10-28
MonotonicityNot monotonic
2024-11-08T04:50:38.189185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.14 × 10-161260
 
1.2%
1.07 × 10-161260
 
1.2%
1.04 × 10-151260
 
1.2%
1.33 × 10-161260
 
1.2%
1.23 × 10-161260
 
1.2%
8.11 × 10-17840
 
0.8%
1.11 × 10-16840
 
0.8%
1.71 × 10-16840
 
0.8%
1.72 × 10-16840
 
0.8%
3.63 × 10-16840
 
0.8%
Other values (209) 94495
90.0%
ValueCountFrequency (%)
1.16 × 10-17420
0.4%
1.21 × 10-17420
0.4%
1.27 × 10-17420
0.4%
1.34 × 10-17420
0.4%
1.46 × 10-17420
0.4%
1.54 × 10-17420
0.4%
1.59 × 10-17420
0.4%
1.62 × 10-17420
0.4%
1.67 × 10-17420
0.4%
1.77 × 10-17420
0.4%
ValueCountFrequency (%)
7.26 × 10-14420
0.4%
6.67 × 10-14420
0.4%
6.41 × 10-14420
0.4%
5.65 × 10-14420
0.4%
5.19 × 10-14420
0.4%
4.99 × 10-14420
0.4%
4.03 × 10-14420
0.4%
3.74 × 10-14420
0.4%
3.71 × 10-14420
0.4%
3.56 × 10-14420
0.4%

PR_NCC
Real number (ℝ)

High correlation 

Distinct78785
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5748646
Minimum0.24823
Maximum99.6453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2024-11-08T04:50:38.802961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.24823
5-th percentile0.498604
Q10.962935
median2.8514
Q37.85695
95-th percentile16.50155
Maximum99.6453
Range99.39707
Interquartile range (IQR)6.894015

Descriptive statistics

Standard deviation7.7407705
Coefficient of variation (CV)1.3885127
Kurtosis26.152723
Mean5.5748646
Median Absolute Deviation (MAD)2.18521
Skewness4.1731596
Sum585332.91
Variance59.919528
MonotonicityNot monotonic
2024-11-08T04:50:39.416151image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0146 11
 
< 0.1%
1.0131 11
 
< 0.1%
1.0388 10
 
< 0.1%
1.1589 10
 
< 0.1%
1.0278 9
 
< 0.1%
1.0553 9
 
< 0.1%
1.044 9
 
< 0.1%
1.0225 9
 
< 0.1%
1.1273 9
 
< 0.1%
1.0058 9
 
< 0.1%
Other values (78775) 104899
99.9%
ValueCountFrequency (%)
0.24823 1
< 0.1%
0.25106 1
< 0.1%
0.25274 1
< 0.1%
0.2532 1
< 0.1%
0.2539 1
< 0.1%
0.25567 1
< 0.1%
0.25611 1
< 0.1%
0.25675 1
< 0.1%
0.25761 1
< 0.1%
0.25781 1
< 0.1%
ValueCountFrequency (%)
99.6453 1
< 0.1%
98.4928 1
< 0.1%
97.3231 1
< 0.1%
96.1354 1
< 0.1%
94.9291 1
< 0.1%
93.7034 1
< 0.1%
93.5979 1
< 0.1%
92.5403 1
< 0.1%
92.4575 1
< 0.1%
91.4661 1
< 0.1%

MC
Real number (ℝ)

High correlation 

Distinct8400
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5900.2422
Minimum6.4174
Maximum42747.402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2024-11-08T04:50:39.971977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum6.4174
5-th percentile143.4232
Q1832.6946
median2336.7172
Q38991.3729
95-th percentile21675.011
Maximum42747.402
Range42740.985
Interquartile range (IQR)8158.6783

Descriptive statistics

Standard deviation7309.3989
Coefficient of variation (CV)1.2388303
Kurtosis2.6474224
Mean5900.2422
Median Absolute Deviation (MAD)2035.8616
Skewness1.7023175
Sum6.1949593 × 108
Variance53427312
MonotonicityNot monotonic
2024-11-08T04:50:40.555838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3607.9704 20
 
< 0.1%
1112.3421 20
 
< 0.1%
158.8149 20
 
< 0.1%
480.2439 20
 
< 0.1%
806.7464 20
 
< 0.1%
1138.3323 20
 
< 0.1%
1475.0111 20
 
< 0.1%
162.2503 20
 
< 0.1%
490.6813 20
 
< 0.1%
824.361 20
 
< 0.1%
Other values (8390) 104795
99.8%
ValueCountFrequency (%)
6.4174 18
< 0.1%
6.9041 20
< 0.1%
7.3429 20
< 0.1%
7.7432 20
< 0.1%
8.1121 20
< 0.1%
8.4547 20
< 0.1%
8.7751 20
< 0.1%
9.0763 20
< 0.1%
9.3609 20
< 0.1%
9.6309 20
< 0.1%
ValueCountFrequency (%)
42747.4022 10
< 0.1%
42104.4147 10
< 0.1%
41442.8121 10
< 0.1%
41261.638 10
< 0.1%
40761.2006 10
< 0.1%
40640.8096 10
< 0.1%
40058.0149 10
< 0.1%
40002.0155 10
< 0.1%
39747.8643 10
< 0.1%
39343.9105 10
< 0.1%

Interactions

2024-11-08T04:50:23.295792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:31.544738image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:39.106153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:46.140062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:49.558127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:53.148859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:58.002232image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:01.123959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:04.308583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:08.275764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:13.132564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:16.287412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:19.638718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:23.659327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:32.117192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:39.918234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:46.402806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:49.840456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:53.527570image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:58.251602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:01.371836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:04.567595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:08.695456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:13.390245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:16.549932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:19.912066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:24.057104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:32.619241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:40.854506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:46.651812image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:50.120072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:53.940159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:58.494276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:01.625971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:04.864658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:09.110527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:13.639566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:16.834917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:20.161587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:24.410513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:32.978960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:41.578349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:46.866062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:50.357415image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:54.332851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:58.728305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:01.879229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:05.116801image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:09.494330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:13.878354image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:17.068163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:20.386593image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:24.809969image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:33.538855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:42.301540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:47.132610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:50.609504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:54.640405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:58.974176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:02.132274image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:05.376480image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:09.854797image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:14.132701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:17.321902image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:20.636232image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:25.172944image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:34.024447image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:42.763840image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:47.366356image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:50.884320image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:54.989666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:59.201233image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:02.381896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:05.633067image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:10.244121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:14.391916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:17.602467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:20.896463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:25.518615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:34.500884image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:43.182132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:47.852839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:51.121958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:55.296923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:59.420077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:02.597653image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:05.907378image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:10.599335image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:14.601698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:17.867904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:21.134389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:25.897830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:34.797405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:43.628574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:48.085803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:51.375131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:55.645132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:59.664435image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:02.856979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:06.159690image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:10.927975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:14.852737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:18.107928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:21.739458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:26.288907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:35.791606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:44.231364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:48.349941image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:51.635628image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:56.013623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:59.941868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:03.115622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:06.428803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:11.291408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:15.107816image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:18.373795image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:21.996242image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:26.668981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:36.276464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:44.823769image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:48.593849image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:51.893146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:56.377019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:00.171562image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:03.350511image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:06.682717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:11.644648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:15.333958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:18.630629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:22.243827image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:27.051185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:37.120356image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:45.235774image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:48.842566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:52.116953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:56.728158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:00.385042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:03.570665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:06.963845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:12.001563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:15.569212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:18.891475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:22.461706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:27.407141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:37.840659image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:45.619039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:49.089048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:52.467347image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:57.437770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:00.642572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:03.860577image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:07.529721image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:12.412030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:15.836717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:19.146837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:22.732543image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:27.788745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:38.424045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:45.895794image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:49.338677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:52.820657image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:49:57.764976image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:00.902386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:04.086916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:07.879116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:12.791738image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:16.059275image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:19.388247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T04:50:22.977951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-08T04:50:40.928267image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
CDIDOLLDL_CYLMCM_NAMEPER_FITPOPR_NCCSASA_CYLSA_DTL
C1.0000.8770.8750.758-0.0000.6140.4010.0000.000-0.000-0.2970.9730.8840.8750.000
DI0.8771.0000.9980.408-0.4210.2250.3190.0000.000-0.000-0.3100.7640.5940.9980.000
DO0.8750.9981.0000.407-0.4190.2240.3480.0000.030-0.000-0.3080.7650.5941.0000.038
L0.7580.4080.4071.0000.5850.9770.4080.0000.000-0.000-0.2010.8770.9690.4070.000
LD-0.000-0.421-0.4190.5851.0000.7210.1140.0000.000-0.0000.0790.1940.402-0.4190.000
L_CYL0.6140.2250.2240.9770.7211.0000.3760.0000.000-0.000-0.1540.7640.9010.2240.000
MC0.4010.3190.3480.4080.1140.3761.0000.405-0.121-0.000-0.5560.4420.4390.3480.294
M_NAME0.0000.0000.0000.0000.0000.0000.4051.0000.1240.0000.2090.0000.0000.0000.000
PER_FIT0.0000.0000.0300.0000.0000.000-0.1210.1241.000-0.4080.2400.0230.0150.0300.219
PO-0.000-0.000-0.000-0.000-0.000-0.000-0.0000.000-0.4081.0000.520-0.000-0.000-0.0000.000
PR_NCC-0.297-0.310-0.308-0.2010.079-0.154-0.5560.2090.2400.5201.000-0.276-0.242-0.3080.016
SA0.9730.7640.7650.8770.1940.7640.4420.0000.023-0.000-0.2761.0000.9640.7650.036
SA_CYL0.8840.5940.5940.9690.4020.9010.4390.0000.015-0.000-0.2420.9641.0000.5940.023
SA_D0.8750.9981.0000.407-0.4190.2240.3480.0000.030-0.000-0.3080.7650.5941.0000.033
TL0.0000.0000.0380.0000.0000.0000.2940.0000.2190.0000.0160.0360.0230.0331.000

Missing values

2024-11-08T04:50:28.261759image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-08T04:50:28.853563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CLDDILL_CYLTLDOSA_DSA_CYLSAPOM_NAMEPER_FITPR_NCCMC
012.091.3913182.782591.3913193.391327400.746426813.951754214.6981101K-X090865.180000e-172.287676.0334
112.091.3913182.782591.3913193.391327400.746426813.951754214.6981201K-X090863.140000e-172.757676.0334
212.091.3913182.782591.3913193.391327400.746426813.951754214.6981301K-X090862.450000e-173.227676.0334
312.091.3913182.782591.3913193.391327400.746426813.951754214.6981401K-X090862.110000e-173.697676.0334
412.091.3913182.782591.3913193.391327400.746426813.951754214.6981501K-X090861.900000e-174.167776.0334
512.091.3913182.782591.3913193.391327400.746426813.951754214.6981601K-X090861.770000e-174.637776.0334
612.091.3913182.782591.3913193.391327400.746426813.951754214.6981701K-X090861.670000e-175.107776.0334
712.091.3913182.782591.3913193.391327400.746426813.951754214.6981801K-X090861.590000e-175.577776.0334
812.091.3913182.782591.3913193.391327400.746426813.951754214.6981901K-X090861.540000e-176.047776.0334
912.091.3913182.782591.3913397.391329798.190427962.408357760.59861K-X090861.250000e-141.9364234.0699
CLDDILL_CYLTLDOSA_DSA_CYLSAPOM_NAMEPER_FITPR_NCCMC
1049851916.0350.08322100.49891750.41589368.0832425639.33822024123.6312449762.9691B55006.410000e-140.737113607.9704
1049861916.0350.08322100.49891750.41589368.0832425639.33822024123.6312449762.969101B55001.510000e-151.748203607.9704
1049871916.0350.08322100.49891750.41589368.0832425639.33822024123.6312449762.969201B55001.190000e-152.759303607.9704
1049881916.0350.08322100.49891750.41589368.0832425639.33822024123.6312449762.969301B55001.090000e-153.770503607.9704
1049891916.0350.08322100.49891750.41589368.0832425639.33822024123.6312449762.969401B55001.040000e-154.781603607.9704
1049901916.0350.08322100.49891750.41589368.0832425639.33822024123.6312449762.969501B55001.010000e-155.792703607.9704
1049911916.0350.08322100.49891750.41589368.0832425639.33822024123.6312449762.969601B55009.850000e-166.803803607.9704
1049921916.0350.08322100.49891750.41589368.0832425639.33822024123.6312449762.969701B55009.700000e-167.814903607.9704
1049931916.0350.08322100.49891750.41589368.0832425639.33822024123.6312449762.969801B55009.590000e-168.826103607.9704
1049941916.0350.08322100.49891750.41589368.0832425639.33822024123.6312449762.969901B55009.500000e-169.837203607.9704